Library

library(plotly)
library(csaw)
library(edgeR)
library(ComplexHeatmap)
library(SummarizedExperiment)

Data

ATAC

load("./input/atac_diff_chromatin_accessibility.RData")

atac_tmm <- data
atac_loess <- offsets

RNA-Seq

load("input/data_pData.RData")
rna_tmm <- data

CPM

ATAC

TMM

cpm_atac <- cpm(asDGEList(atac_tmm))
colnames(cpm_atac) <- gsub(pattern = ".*/|\\.bam", replacement = "", x = colData(atac_tmm)[, 1])
rownames(cpm_atac) <- rowData(atac_tmm)[, 6]
cpm_atac <- data.frame(Peak = rownames(cpm_atac), Genes = rowData(atac_tmm)[, "Peak-SYMBOL"], cpm_atac)

Loess

loess_atac <- log1p(assay(atac_loess, i = 1)) - assay(atac_loess, i = 2)
colnames(loess_atac) <- gsub(pattern = ".*/|\\.bam", replacement = "", x = colData(atac_loess)[, 1])
rownames(loess_atac) <- rowData(atac_loess)[, 6]
loess_atac <- data.frame(Peak = rownames(loess_atac), Genes = rowData(atac_loess)[, "Peak-SYMBOL"], loess_atac)

Peaks

dar_tmm <- data.frame(rowData(atac_tmm), stringsAsFactors = F, check.names = F)

peaks_tmm <- dar_tmm[abs(dar_tmm$`diffAccessibility-logFC`) >= 1 &
  dar_tmm$`diffAccessibility-qvalue` <= 0.05, 6]

dar_loess <- data.frame(rowData(atac_loess), stringsAsFactors = F, check.names = F)
peaks_loess <- dar_loess[abs(dar_loess$`diffAccessibility-logFC`) >= 1 &
  dar_loess$`diffAccessibility-qvalue` <= 0.05, 6]

peaks_common <- intersect(peaks_tmm, peaks_loess)

peaks_diff <- setdiff(peaks_tmm, peaks_loess)

Expression

rna_cpm_df <- data$cpm
rna_cpm_df <- rna_cpm_df[, grep(pattern = "PND8", x = colnames(rna_cpm_df), invert = TRUE)]
rna_cpm_df <- data.frame(Genes = rownames(rna_cpm_df), rna_cpm_df)
colnames(rna_cpm_df)[2:ncol(rna_cpm_df)] <- paste0("RS_", colnames(rna_cpm_df)[2:ncol(rna_cpm_df)])

Merging Expression with ATAC

TMM

ca <- data.frame(distanceTSS = rowData(atac_tmm)[, "Peak-distanceToTSS"], cpm_atac)
ca <- ca[abs(ca$distanceTSS) <= 5000, ]
cpm_atac_rna <- merge(ca[, -1], rna_cpm_df)
rownames(cpm_atac_rna) <- cpm_atac_rna$Peak

cpm_rna <- cpm_atac_rna[, grep(pattern = "RS_", x = colnames(cpm_atac_rna), invert = T)]
rna_cpm <- cpm_atac_rna[, grep(pattern = "Genes|Peak|RS_", x = colnames(cpm_atac_rna))]
colnames(rna_cpm) <- gsub(pattern = "RS_", replacement = "", x = colnames(rna_cpm))

Loess

la <- data.frame(distanceTSS = rowData(atac_loess)[, "Peak-distanceToTSS"], loess_atac)
la <- la[abs(la$distanceTSS) <= 5000, ]
loess_atac_rna <- merge(la[, -1], rna_cpm_df)
rownames(loess_atac_rna) <- loess_atac_rna$Peak

loess_rna <- loess_atac_rna[, grep(pattern = "RS_", x = colnames(loess_atac_rna), invert = T)]
rna_loess <- loess_atac_rna[, grep(pattern = "Genes|Peak|RS_", x = colnames(loess_atac_rna))]
colnames(rna_loess) <- gsub(pattern = "RS_", replacement = "", x = colnames(rna_loess))

Peaks overlapping

peaks_tmm_rna <- rownames(cpm_atac_rna)[rownames(cpm_atac_rna) %in% peaks_tmm]

peaks_loess_rna <- rownames(cpm_atac_rna)[rownames(cpm_atac_rna) %in% peaks_loess]

peaks_common_rna <- rownames(cpm_atac_rna)[rownames(cpm_atac_rna) %in% peaks_common]

peaks_sd_rna <- rownames(cpm_atac_rna)[rownames(cpm_atac_rna) %in% peaks_diff]

Peaks table

df_peaks <- data.frame(
  Group = c("TMM", "Loess", "Common", "setDiff"),
  n = c(
    length(peaks_tmm), length(peaks_loess),
    length(peaks_common), length(peaks_diff)
  ),
  overlapRNA_dissTSS_5k = c(
    length(peaks_tmm_rna), length(peaks_loess_rna),
    length(peaks_common_rna), length(peaks_sd_rna)
  )
)

knitr::kable(x = df_peaks, format = "html", align = "c", row.names = FALSE)
Group n overlapRNA_dissTSS_5k
TMM 3212 654
Loess 2901 590
Common 2580 529
setDiff 632 125

Heatmaps

anno_atac <- data.frame(Group = colnames(cpm_atac)[-c(1:2)])
rownames(anno_atac) <- anno_atac$Group
anno_atac$Group <- gsub(pattern = "_.*", replacement = "", x = anno_atac$Group)

an_atac <- HeatmapAnnotation(
  Groups = anno_atac$Group,
  col = list(Groups = c("PND15" = "darkgreen", "Adult" = "Maroon"))
)

anno_rna <- rna_tmm$pData[, 1, drop = FALSE]
anno_rna <- anno_rna[grep(pattern = "PND8", x = rownames(anno_rna), invert = T), , drop = FALSE]

an_rna <- HeatmapAnnotation(
  Groups = anno_rna$Group,
  col = list(Groups = c("PND15" = "darkgreen", "Adult" = "Maroon"))
)

TMM peaks

TMM + Loess

tmm_peak_1 <- Heatmap(
  matrix = t(scale(t(cpm_atac[peaks_tmm, -c(1:2)]))),
  column_title = "TMM Normalization", name = "AS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(8, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

tmm_peak_2 <- Heatmap(
  matrix = t(scale(t(loess_atac[peaks_tmm, -c(1:2)]))),
  column_title = "Loess Normalization", name = "AS: Loess",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(8, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

draw(tmm_peak_1 + tmm_peak_2)

RNA + TMM + Loess

tmm_peak_3 <- Heatmap(
  matrix = t(scale(t(rna_cpm[peaks_tmm_rna, -c(1:2)]))),
  column_title = "Gene expression", name = "RS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_rna
)


tmm_peak_4 <- Heatmap(
  matrix = t(scale(t(cpm_rna[peaks_tmm_rna, -c(1:2)]))),
  column_title = "TMM Normalization", name = "AS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

tmm_peak_5 <- Heatmap(
  matrix = t(scale(t(loess_rna[peaks_tmm_rna, -c(1:2)]))),
  column_title = "Loess Normalization", name = "AS: Loess",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)


draw(tmm_peak_3 + tmm_peak_4 + tmm_peak_5)

Loess peaks

TMM + Loess

loess_peak_1 <- Heatmap(
  matrix = t(scale(t(cpm_atac[peaks_loess, -c(1:2)]))),
  column_title = "TMM Normalization", name = "AS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(8, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

loess_peak_2 <- Heatmap(
  matrix = t(scale(t(loess_atac[peaks_loess, -c(1:2)]))),
  column_title = "Loess Normalization", name = "AS: Loess",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(8, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

draw(loess_peak_1 + loess_peak_2)

RNA + TMM + Loess

loess_peak_3 <- Heatmap(
  matrix = t(scale(t(rna_loess[peaks_loess_rna, -c(1:2)]))),
  column_title = "Gene expression", name = "RS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_rna
)


loess_peak_4 <- Heatmap(
  matrix = t(scale(t(cpm_rna[peaks_loess_rna, -c(1:2)]))),
  column_title = "TMM Normalization", name = "AS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

loess_peak_5 <- Heatmap(
  matrix = t(scale(t(loess_rna[peaks_loess_rna, -c(1:2)]))),
  column_title = "Loess Normalization", name = "AS: Loess",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)


draw(loess_peak_3 + loess_peak_4 + loess_peak_5)

Common peaks

TMM + Loess

c_peak_1 <- Heatmap(
  matrix = t(scale(t(cpm_atac[peaks_common, -c(1:2)]))),
  column_title = "TMM Normalization", name = "AS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(8, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

c_peak_2 <- Heatmap(
  matrix = t(scale(t(loess_atac[peaks_common, -c(1:2)]))),
  column_title = "Loess Normalization", name = "AS: Loess",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(8, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

draw(c_peak_1 + c_peak_2)

RNA + TMM + Loess

c_peak_3 <- Heatmap(
  matrix = t(scale(t(rna_cpm[peaks_common_rna, -c(1:2)]))),
  column_title = "Gene expression", name = "RS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_rna
)


c_peak_4 <- Heatmap(
  matrix = t(scale(t(cpm_rna[peaks_common_rna, -c(1:2)]))),
  column_title = "TMM Normalization", name = "AS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

c_peak_5 <- Heatmap(
  matrix = t(scale(t(loess_rna[peaks_common_rna, -c(1:2)]))),
  column_title = "Loess Normalization", name = "AS: Loess",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)


draw(c_peak_3 + c_peak_4 + c_peak_5)

setDiff peaks

TMM + Loess

sd_peak_1 <- Heatmap(
  matrix = t(scale(t(cpm_atac[peaks_diff, -c(1:2)]))),
  column_title = "TMM Normalization", name = "AS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(8, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

sd_peak_2 <- Heatmap(
  matrix = t(scale(t(loess_atac[peaks_diff, -c(1:2)]))),
  column_title = "Loess Normalization", name = "AS: Loess",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(8, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

draw(sd_peak_1 + sd_peak_2)

RNA + TMM + Loess

sd_peak_3 <- Heatmap(
  matrix = t(scale(t(rna_cpm[peaks_sd_rna, -c(1:2)]))),
  column_title = "Gene expression", name = "RS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_rna
)


sd_peak_4 <- Heatmap(
  matrix = t(scale(t(cpm_rna[peaks_sd_rna, -c(1:2)]))),
  column_title = "TMM Normalization", name = "AS: TMM",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)

sd_peak_5 <- Heatmap(
  matrix = t(scale(t(loess_rna[peaks_sd_rna, -c(1:2)]))),
  column_title = "Loess Normalization", name = "AS: Loess",
  show_row_names = FALSE, cluster_columns = FALSE,
  na_col = "grey", km = 2, show_column_names = FALSE,
  width = unit(6, "cm"), height = unit(12, "cm"),
  top_annotation = an_atac
)


draw(sd_peak_3 + sd_peak_4 + sd_peak_5)

Plots of logFC values

Date

dar_tmm_s <- dar_tmm[,grep(pattern = "Name|diff", x = colnames(dar_tmm))]
colnames(dar_tmm_s) <- gsub(pattern = "diffAccessibility", replacement = "TMM", x = colnames(dar_tmm_s))

dar_loess_s <- dar_loess[,grep(pattern = "Name|diff", x = colnames(dar_loess))]
colnames(dar_loess_s) <- gsub(pattern = "diffAccessibility", replacement = "Loess", x = colnames(dar_loess_s))

dar_tab <- merge(dar_tmm_s, dar_loess_s)
rownames(dar_tab) <- dar_tab$`Peak-Name`

Function to make plot

logFC_plot <- function(res){
  ggplot(data = res, aes(x = `TMM-logFC`, y = `Loess-logFC`)) +
  geom_point(alpha = 0.2, size = 1) +
  ggtitle("Log2 Fold Change") +
  labs(subtitle="lm fit") +
  xlab("TMM normalization") +
  ylab("Loess normalization") +
  theme(
    plot.title = element_text(face = "bold", size = 20, hjust = 0.5),
    plot.subtitle=element_text(size=16, hjust=0.5, face="italic", color="blue"),
    axis.title.x = element_text(face = "bold", size = 15),
    axis.text.x = element_text(face = "bold", size = 12),
    axis.title.y = element_text(face = "bold", size = 15),
    axis.text.y = element_text(face = "bold", size = 12),
    legend.title = element_text(face = "bold", size = 15),
    legend.text = element_text(size = 14)
  ) +
  stat_smooth(se = FALSE, method = "lm", color = "red", formula = y ~ x, size = 0.5)
}

Results

All Peaks

logFC_plot(res = dar_tab)

TMM peaks

logFC_plot(res = dar_tab[peaks_tmm,])

Loess peaks

logFC_plot(res = dar_tab[peaks_loess,])

Common peaks

logFC_plot(res = dar_tab[peaks_common,])

Diff peaks

logFC_plot(res = dar_tab[peaks_diff,])

SessionInfo

devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.2 (2019-12-12)
##  os       Ubuntu 16.04.6 LTS          
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Europe/Zurich               
##  date     2020-05-20                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package              * version    date       lib
##  AnnotationDbi          1.48.0     2019-10-29 [1]
##  askpass                1.1        2019-01-13 [1]
##  assertthat             0.2.1      2019-03-21 [1]
##  backports              1.1.6      2020-04-05 [1]
##  Biobase              * 2.46.0     2019-10-29 [1]
##  BiocFileCache          1.10.2     2019-11-08 [1]
##  BiocGenerics         * 0.32.0     2019-10-29 [1]
##  BiocParallel         * 1.20.1     2019-12-21 [1]
##  biomaRt                2.42.1     2020-03-26 [1]
##  Biostrings             2.54.0     2019-10-29 [1]
##  bit                    1.1-15.2   2020-02-10 [1]
##  bit64                  0.9-7      2017-05-08 [1]
##  bitops                 1.0-6      2013-08-17 [1]
##  blob                   1.2.1      2020-01-20 [1]
##  bookdown               0.18       2020-03-05 [1]
##  callr                  3.4.3      2020-03-28 [1]
##  circlize               0.4.8      2019-09-08 [1]
##  cli                    2.0.2      2020-02-28 [1]
##  clue                   0.3-57     2019-02-25 [1]
##  cluster                2.1.0      2019-06-19 [1]
##  colorspace             1.4-1      2019-03-18 [1]
##  ComplexHeatmap       * 2.2.0      2019-10-29 [1]
##  crayon                 1.3.4      2017-09-16 [1]
##  csaw                 * 1.20.0     2019-10-29 [1]
##  curl                   4.3        2019-12-02 [1]
##  data.table             1.12.8     2019-12-09 [1]
##  DBI                    1.1.0      2019-12-15 [1]
##  dbplyr                 1.4.3      2020-04-19 [1]
##  DelayedArray         * 0.12.3     2020-04-09 [1]
##  desc                   1.2.0      2018-05-01 [1]
##  devtools               2.3.0      2020-04-10 [1]
##  digest                 0.6.25     2020-02-23 [1]
##  dplyr                  0.8.5      2020-03-07 [1]
##  edgeR                * 3.28.1     2020-02-26 [1]
##  ellipsis               0.3.0      2019-09-20 [1]
##  evaluate               0.14       2019-05-28 [1]
##  fansi                  0.4.1      2020-01-08 [1]
##  farver                 2.0.3      2020-01-16 [1]
##  fs                     1.4.1      2020-04-04 [1]
##  GenomeInfoDb         * 1.22.1     2020-03-27 [1]
##  GenomeInfoDbData       1.2.2      2019-11-18 [1]
##  GenomicAlignments      1.22.1     2019-11-12 [1]
##  GenomicFeatures        1.38.2     2020-02-15 [1]
##  GenomicRanges        * 1.38.0     2019-10-29 [1]
##  GetoptLong             0.1.8      2020-01-08 [1]
##  ggplot2              * 3.3.0      2020-03-05 [1]
##  GlobalOptions          0.1.1      2019-09-30 [1]
##  glue                   1.4.0      2020-04-03 [1]
##  gtable                 0.3.0      2019-03-25 [1]
##  highr                  0.8        2019-03-20 [1]
##  hms                    0.5.3      2020-01-08 [1]
##  htmltools              0.4.0      2019-10-04 [1]
##  htmlwidgets            1.5.1      2019-10-08 [1]
##  httr                   1.4.1      2019-08-05 [1]
##  IRanges              * 2.20.2     2020-01-13 [1]
##  jsonlite               1.6.1      2020-02-02 [1]
##  knitr                  1.28       2020-02-06 [1]
##  labeling               0.3        2014-08-23 [1]
##  lattice                0.20-41    2020-04-02 [1]
##  lazyeval               0.2.2      2019-03-15 [1]
##  lifecycle              0.2.0      2020-03-06 [1]
##  limma                * 3.42.2     2020-02-03 [1]
##  locfit                 1.5-9.4    2020-03-25 [1]
##  magrittr               1.5        2014-11-22 [1]
##  Matrix                 1.2-18     2019-11-27 [1]
##  matrixStats          * 0.56.0     2020-03-13 [1]
##  memoise                1.1.0.9000 2020-05-06 [1]
##  mgcv                   1.8-31     2019-11-09 [1]
##  munsell                0.5.0      2018-06-12 [1]
##  nlme                   3.1-147    2020-04-13 [1]
##  openssl                1.4.1      2019-07-18 [1]
##  pillar                 1.4.4      2020-05-05 [1]
##  pkgbuild               1.0.7      2020-04-25 [1]
##  pkgconfig              2.0.3      2019-09-22 [1]
##  pkgload                1.0.2      2018-10-29 [1]
##  plotly               * 4.9.2.1    2020-04-04 [1]
##  png                    0.1-7      2013-12-03 [1]
##  prettyunits            1.1.1      2020-01-24 [1]
##  processx               3.4.2      2020-02-09 [1]
##  progress               1.2.2      2019-05-16 [1]
##  ps                     1.3.2      2020-02-13 [1]
##  purrr                  0.3.4      2020-04-17 [1]
##  R6                     2.4.1      2019-11-12 [1]
##  rappdirs               0.3.1      2016-03-28 [1]
##  RColorBrewer           1.1-2      2014-12-07 [1]
##  Rcpp                   1.0.4.6    2020-04-09 [1]
##  RCurl                  1.98-1.2   2020-04-18 [1]
##  remotes                2.1.1      2020-02-15 [1]
##  rjson                  0.2.20     2018-06-08 [1]
##  rlang                  0.4.6      2020-05-02 [1]
##  rmarkdown              2.1        2020-01-20 [1]
##  rmdformats             0.3.7      2020-03-11 [1]
##  rprojroot              1.3-2      2018-01-03 [1]
##  Rsamtools              2.2.3      2020-02-23 [1]
##  RSQLite                2.1.4      2019-12-04 [1]
##  rtracklayer            1.46.0     2019-10-29 [1]
##  S4Vectors            * 0.24.4     2020-04-09 [1]
##  scales                 1.1.0      2019-11-18 [1]
##  sessioninfo            1.1.1      2018-11-05 [1]
##  shape                  1.4.4      2018-02-07 [1]
##  stringi                1.4.6      2020-02-17 [1]
##  stringr                1.4.0      2019-02-10 [1]
##  SummarizedExperiment * 1.16.1     2019-12-19 [1]
##  testthat               2.3.2      2020-03-02 [1]
##  tibble                 3.0.1      2020-04-20 [1]
##  tidyr                  1.0.2      2020-01-24 [1]
##  tidyselect             1.0.0      2020-01-27 [1]
##  usethis                1.6.1      2020-04-29 [1]
##  vctrs                  0.2.4      2020-03-10 [1]
##  viridisLite            0.3.0      2018-02-01 [1]
##  withr                  2.2.0      2020-04-20 [1]
##  xfun                   0.13       2020-04-13 [1]
##  XML                    3.99-0.3   2020-01-20 [1]
##  XVector                0.26.0     2019-10-29 [1]
##  yaml                   2.2.1      2020-02-01 [1]
##  zlibbioc               1.32.0     2019-10-29 [1]
##  source                        
##  Bioconductor                  
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  Bioconductor                  
##  Bioconductor                  
##  Bioconductor                  
##  Bioconductor                  
##  Bioconductor                  
##  Bioconductor                  
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  Bioconductor                  
##  CRAN (R 3.6.1)                
##  Bioconductor                  
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  Bioconductor                  
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  Bioconductor                  
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  Bioconductor                  
##  Bioconductor                  
##  Bioconductor                  
##  Bioconductor                  
##  Bioconductor                  
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  Bioconductor                  
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  Bioconductor                  
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  Github (r-lib/memoise@4aefd9f)
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  Bioconductor                  
##  CRAN (R 3.6.1)                
##  Bioconductor                  
##  Bioconductor                  
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  Bioconductor                  
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.1)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  CRAN (R 3.6.2)                
##  Bioconductor                  
##  CRAN (R 3.6.2)                
##  Bioconductor                  
## 
## [1] /home/ubuntu/R/x86_64-pc-linux-gnu-library/3.6
## [2] /usr/local/lib/R/site-library
## [3] /usr/lib/R/site-library
## [4] /usr/lib/R/library